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Modelling and Forecasting Daily International Mass Tourism to Peru

  • Jose Angelo Divino

    (Department of Economics, Catholic University of Brasilia)

  • Michael McAleer

    (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute and Center for International Research on the Japanese Economy (CIRJE), Faculty of Economics, University of Tokyo)

Peru is a South American country that is divided into two parts by the Andes Mountains. The rich historical, cultural and geographic diversity has led to the inclusion of ten Peruvian sites on UNESCO's World Heritage List. For the potentially negative impacts of mass tourism on the environment, and hence on future international tourism demand, to be managed appropriately require modelling growth rates and volatility adequately. The paper models the growth rate and volatility (or the variability in the growth rate) in daily international tourist arrivals to Peru from 1997 to 2007. The empirical results show that international tourist arrivals and their growth rates are stationary, and that the estimated symmetric and asymmetric conditional volatility models all fit the data extremely well. Moreover, the estimates resemble those arising from financial time series data, with both short and long run persistence of shocks to the growth rate in international tourist arrivals.

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File URL: http://www.cirje.e.u-tokyo.ac.jp/research/dp/2009/2009cf651.pdf
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Paper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-651.

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Length: 34 pages
Date of creation: Aug 2009
Date of revision:
Handle: RePEc:tky:fseres:2009cf651
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  8. Michael McAleer, 2009. "The Ten Commandments for Optimizing Value-at-Risk and Daily Capital Charges," CIRJE F-Series CIRJE-F-652, CIRJE, Faculty of Economics, University of Tokyo.
  9. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
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  19. McAleer, Michael, 2005. "Automated Inference And Learning In Modeling Financial Volatility," Econometric Theory, Cambridge University Press, vol. 21(01), pages 232-261, February.
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  22. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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